# MAX Nightly 26.3.0.dev2026042405 (Mojo 1.0.0b1.dev2026042405) Released

**URL:** <https://forum.modular.com/t/max-nightly-26-3-0-dev2026042405-mojo-1-0-0b1-dev2026042405-released/2997>\
**Category:** Nightly\
**Created:** [April 24, 2026, 6:57am UTC](https://forum.modular.com/t/max-nightly-26-3-0-dev2026042405-mojo-1-0-0b1-dev2026042405-released/2997 "2026-04-24T06:57:36Z")\
**Posts on this page:** 1\
**Page:** 1

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**Author:** ![Modular](https://sea1.discourse-cdn.com/flex001/user_avatar/forum.modular.com/modular/32/17_2.png) [@Modular](https://forum.modular.com/u/Modular)\
**Post date:** [April 24, 2026, 6:57am UTC](https://forum.modular.com/t/max-nightly-26-3-0-dev2026042405-mojo-1-0-0b1-dev2026042405-released/2997/1 "2026-04-24T06:57:36Z")

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🧑‍🚀 A new nightly version has been released! 🧑‍🚀

See the quickstart guide for installation instructions: [Quickstart | Modular](https://docs.modular.com/max/get-started/#set-up-your-project)

MAX changelog updates:

- Fixed Wan 2.1 / 2.2 video diffusion pipelines silently running without  
classifier-free guidance. The tokenizer gated negative-prompt tokenization  
on `true_cfg_scale > 1.0` (default `1.0`), so negative tokens were never  
produced and the executor fell back to unguided generation even when  
`guidance_scale > 1.0` and a negative prompt were supplied. Wan now enables  
classical CFG whenever `guidance_scale > 1.0` and defaults an absent  
negative prompt to the empty string, matching the diffusers baseline.
- `max.experimental.Tensor` is now distribution-aware: it carries a  
tuple of per-shard storages, `driver.Buffer`s (realized) or graph  
values (`TensorValue` / `BufferValue`, unrealized), paired with a  
`DeviceMapping` that maps those local shards onto the  
`DeviceMesh`.
- Reworked `max.experimental.functional` from a single `functional.py`  
into a `functional/` package, a new distribution-and mesh-aware  
dispatch layer on top of the graph-compiler Python API, split cleanly  
into three op categories: `creation_ops` (tensor factories), `spmd_ops`  
(rule-based per-op SPMD dispatch), and `collective_ops`  
(`allreduce_sum`, `allgather`, `reduce_scatter` etc., now applied per  
device-group along a chosen mesh axis so they dispatch correctly on  
multi-dimensional meshes, plus a `transfer_to` convenience op  
between `DeviceMapping`s).
- Added `max.experimental.sharding` with the core types for distributed  
tensors (`DeviceMesh`; `DeviceMapping` with `PlacementMapping` and  
`NamedMapping`; placement primitives `Replicated` / `Sharded` /  
`Partial`; `DistributedTensorType` / `DistributedBufferType`;  
`TensorLayout`), plus a `sharding.rules` submodule of pure  
mapping-propagation rules (elementwise, matmul, reduction, shape,  
conv, pooling) that, for each op, either error out or reshard inputs  
to the proposed `DeviceMapping`s and derive the resulting output  
`DeviceMapping`.
- `max.experimental.nn.Module.compile()` now accepts  
`DistributedTensorType` symbolic inputs (not just `TensorType`), so  
distributed models can be built via the graph-compilation path in  
addition to running eagerly; `gemma3_modulev3` is the first multi-GPU  
model wired up. DTensor support in MAX is still ongoing work and  
these APIs may evolve.

Mojo changelog updates:

- [stdlib] feat: Add `forget_deinit()` wrapper around `lit.ownership.mark_destroyed`
- [mojo-lang] Make `xhh`/`ooo` denote code points

Raw MAX diff: [https://github.com/modular/modular/compare/6c6ae42721413f0c5c33a2cdba7f2f5e23d5a846...9f74eec755adc00adb08e377e1dff8e3b0523482](https://github.com/modular/modular/compare/6c6ae42721413f0c5c33a2cdba7f2f5e23d5a846...9f74eec755adc00adb08e377e1dff8e3b0523482))\>  
Current Mojo changelog: [https://github.com/modular/modular/blob/main/mojo/docs/nightly-changelog.md](https://github.com/modular/modular/blob/main/mojo/docs/nightly-changelog.md)